Fundamentals of speech recognition
Fundamentals of speech recognition
Wavelet transformation and pre-selection of mother wavelets for ECG signal processing
BioMed'06 Proceedings of the 24th IASTED international conference on Biomedical engineering
On the choice of the wavelets for ECG data compression
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
ECG data provisioning for telehomecare monitoring
Proceedings of the 2008 ACM symposium on Applied computing
Incremental HMM training applied to ECG signal analysis
Computers in Biology and Medicine
Analysis of Myocardial Infarction Using Discrete Wavelet Transform
Journal of Medical Systems
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This work aims at providing new insights on the electrocardiogram (ECG) segmentation problem using wavelets. The wavelet transform has been originally combined with a hidden Markov models (HMMs) framework in order to carry out beat segmentation and classification. A group of five continuous wavelet functions commonly used in ECG analysis has been implemented and compared using the same framework. All experiments were realized on the QT database, which is composed of a representative number of ambulatory recordings of several individuals and is supplied with manual labels made by a physician. Our main contribution relies on the consistent set of experiments performed. Moreover, the results obtained in terms of beat segmentation and premature ventricular beat (PVC) detection are comparable to others works reported in the literature, independently of the type of the wavelet. Finally, through an original concept of combining two wavelet functions in the segmentation stage, we achieve our best performances.